MCP server

Citehound

io.github.andacuzel/citehound

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Scan any domain for AI readiness and generate llms.txt, robots.txt and schema. Free, read-only.

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

13

Token cost

About 3,278 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http citehound https://getcitehound.com/api/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.citehound]
url = "https://getcitehound.com/api/mcp"

Gemini CLI

{"mcpServers":{"citehound":{"httpUrl":"https://getcitehound.com/api/mcp"}}}

Cursor

{"mcpServers":{"citehound":{"url":"https://getcitehound.com/api/mcp"}}}

VS Code

{"servers":{"citehound":{"type":"http","url":"https://getcitehound.com/api/mcp"}}}

Antigravity

agy mcp add citehound https://getcitehound.com/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"citehound","server_url":"https://getcitehound.com/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"citehound","server_url":"https://getcitehound.com/api/mcp"}

Tools

• compare_sites ~147 tokens
  Scans two domains — one after the other, not in parallel — and reports both scores plus every check where they differ. Useful for comparing your site against a named competitor.
• generate_llms_txt ~252 tokens
  Builds an llms.txt file — an optional convention for describing a site and its key pages, with no demonstrated effect on Google — from a name, a one-sentence description and a list of key pages. Pure text generation — fetches nothing.
• generate_robots_txt ~242 tokens
  Builds a robots.txt file that explicitly allows or blocks named AI crawlers from Citehound’s tracked list of 10, plus an optional Sitemap line. Pure text generation — fetches nothing. Call list_ai_crawlers first if you need the exact tracked crawler names.
• generate_schema ~224 tokens
  Fetches a domain’s homepage for its real title, meta description and declared language, then returns ready-to-paste JSON-LD structured data of the requested type. Fields the scan cannot know — a FAQ’s actual questions, a product’s real price — come back as clearly bracketed placeholders, never inven
• get_benchmark ~178 tokens
  Returns Citehound’s own archived scan data for one category — average, median and range of scores, pillar breakdowns, per-crawler blocked, limited and open counts, and the most common failed checks, with sample size and scan date — or a ranked summary across every category when called with no argume
• get_citation_prompts ~467 tokens
  Returns the set of buying-intent questions Citehound uses to test whether an AI assistant names a brand, for one vertical, plus a short protocol a person can run in their own assistant: ask each question in a separate conversation three to five times, note whether the brand is named and whether it a
• get_citation_sample ~220 tokens
  Returns Citehound’s anonymised sample citation run as readable text: the model, the date, how many times each question was asked, the questions in three groups (named in every try, unstable, never named), the overall rate of being named with its range across sweeps, and a note on the limits. It is o
• get_methodology ~307 tokens
  Explains how the Citehound AI readiness score is built, read from the scanner’s own check registry so it matches what runs. With no argument it returns the three pillars with their weights (they add up to 100), every check with its category, points, what it tests and why it matters, and the limits o
• get_playbook ~298 tokens
  Returns the full GEO/AEO playbook for one of Citehound’s fourteen verticals, as plain readable text: the strategic shift, three actionable strategies, outdated pitfalls to avoid, and an expert-tip placeholder. Called with no argument it returns the list of the fourteen verticals with their track (B2
• get_research ~241 tokens
  Returns Citehound’s published research: with no argument, the list of reports (the llms.txt and crawler study, the AgaOne case study and the benchmark data) with title, date, one-line finding and URL; with a slug, the key findings, the method limits, the URL and a ready line for citing it. Findings
• get_sample_report ~249 tokens
  Returns the sample Pro report as readable text: a real crawl of Citehound’s own site on a stated date, with the results shown as recorded. It gives the whole-site score beside the homepage score, the pillar averages, the priorities from the executive summary with the label saying what wrote it, the
• list_ai_crawlers ~97 tokens
  Lists the 10 AI crawlers Citehound tracks: which company runs each, what allowing or blocking it means, and this project’s robots.txt generator’s default for it. Use before generate_robots_txt if you need the exact tracked names. Fetches nothing.
• scan_site ~356 tokens
  Scans a domain’s public robots.txt, llms.txt, sitemap declaration and homepage, and scores it out of 100 across three pillars: discoverability (can AI crawlers reach it), technical foundation (can machines parse it), and content & trust (does it look like a credible source). Returns the score, the t

Browse

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